The Only Three Questions That Still Count: Investing By Knowing What Others Don't

Summary

Ken Fisher explains what the competition doesn't know

From investment expert and long-time Forbes columnist KenFisher comes the Second Edition of The Only ThreeQuestions That Count. Most investors know the only way to consistently beat the markets is by knowing things others don't.But how can investors consistently find unique information in an increasingly interconnected world?

In this book, Ken Fisher shows investors how they can find more usable information and improve their investing success rate—by answering just three questions.

Packed with more than 100 visuals and practical advice, TheOnly Three Questions That Count is an entertaining and educational guide to the markets. But it also provides a useableframework investors can use now and for the rest of their investing careers.

CNBC's Mad Money host and money manager James J. Cramer says the book "may be the single best thing you could do this year to make yourself a better investor"
Steve Forbes says, "Investors will find this brilliant book an eye-opening, capital-gains producing experience"

The key to improving investing results is daring to challenge yourself and whatever you believe to be true, and Ken Fisher explains how in his own inimitable style.

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The Only Three Questions That Still Count - Kenneth L. Fisher

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Chapter 1

QUESTION ONE: WHAT DO YOU BELIEVE THAT IS ACTUALLY FALSE?

If You Knew It Was Wrong, You Wouldn’t Believe It

It’s safe to assume if you knew something was wrong, you wouldn’t believe it true in the first place. But in a world where so much of industry-applied craft has morphed into long-held mythologies, much of what everyone believes is false. This isn’t any different from long ago when humanity believed the world was flat.

You needn’t beat yourself up if you fall prey to false mythologies. Pretty much everyone has and does. Once you accept that, you can begin gaming everyone else with greater success.

If sorting false mythology from fact were trivial, there wouldn’t be so many false truths. While this isn’t trivial, it isn’t impossible either. One inherent difficulty is this approach requires being skeptical about all your prior beliefs—something most humans dislike. In fact, most humans hate self-questioning and prefer spending time convincing themselves (and others) their beliefs are right. Effectively, you can’t trust any conclusion you thought you knew.

To think through false mythologies, we must first ask: Why do so many people believe things that are false? And why do false truths persist—getting passed down the decades as if they were fact? It comes back to the same point: People persist in believing things that are wrong because, individually, people rarely investigate their own beliefs, particularly when what they believe makes sense intuitively—even more so when those around them agree with them.

As a society, we are often encouraged to challenge someone else’s views, as in, I know those @&%$#! (insert either Republicans or Democrats as you choose) are full of phony views! But we aren’t trained to challenge ourselves or to question the basic nature of the universe the way an Einstein, Edison or Newton would. Our instinct is to accept wisdom passed to us by former generations or smarter people or both. These beliefs don’t require investigation because we believe certain truths are beyond our ability to challenge. Often in life, that is right. I mean, if they can’t figure it out, how could I?

Medicine is a good example. We are correctly conditioned to go to the doctor, describe symptoms, hear prognosis and accept a prescription. Generally, that is good conditioning because medicine is an example of science and craft operating largely in parallel harmony—not perfectly because there are certainly plenty of myths among doctors—but generally because over time science modifies the craft and the craft improves. Because there are so many life examples where our conditioning serves us well, we’re blind to the few areas, like capital markets, where it doesn’t.

There are myriad beliefs you’re likely to share with your fellow investors. These beliefs have been built into decades of literature and are among the first things people learn when they start investing and have been accepted by the biggest names around us. Who are you to question and challenge them?

Exactly the right person!

For example, investors categorically believe when the stock market has a high price-to-earnings ratio (P/E), it’s riskier and has less upside than when it has a low P/E. Think about it casually, and it probably makes sense. A high P/E means a stock (or even the whole market) price is high—way high—compared to earnings. Get too far out on that scale, and it would seem a high P/E means a stock is vastly overpriced and likely to start falling. This belief is so widely held by so many people, seems so logical and has been a basic tenet of investing for so long that if you start proposing to your friends it’s false, you will meet with overwhelming rejection, ridicule and perhaps suggestions you’re morally deficient somehow.

Yet I proved statistically more than 15 years ago the P/E, no matter its level, by itself tells you nothing about market risk or return. Statistics aside, if you delve heavily into theory (as we do later), you will also learn the P/E shouldn’t tell you anything about risk or return anyway. But tell that to people, including the overwhelming bulk of people who have been trained and should know better, and they will think you’re crazy—a real whack-job.

The cool part comes after we accept the truth that P/Es tell you nothing about future returns by themselves—when people are freaking out, fearfully fretting over the market P/E being too high, we can bet against the market falling. While that won’t always work because something else can come along and knock the market down (we cover how to better see that later), it will work much more often than not. In the same way, if the market’s P/E is low and we can sense people are optimistic because of it, we can bet against them also. The key is understanding the truth instead of the mythology. This is basic to the scientific approach.

Many false mythologies—like the P/E one—are accepted widely by the best and brightest minds and passed to the investing public through all forms of media. They don’t inspire questioning from you, me or anyone. We have faith in them, like Catholics do in the Trinity and environmentalists in global warming, and they require no further proof. Holy! Sacred! No one questions these beliefs. No one offers dissenting analysis. And if you do, you’re a heathen. And because there is no dissenting opinion, society feels no need to see proof of these alleged investing truisms with statistically valid data. And mythology continues.

How can it be so few demand hard evidence to support generally accepted investing wisdom? Why do investment decisions not get the scrutiny that car mechanics do? We should be at least as skeptical, if not more so, of the financial industry’s pronouncements. To change the success (or lack thereof) you’ve had so far with investing, be skeptical. Be a cynic. Be the one to point out the emperor wears no clothes. Look around and assess what you and your fellow investors are accepting as truth. But the most important person to be skeptical of is yourself.

Long ago as I read or listened to media, I’d note things I believed were false and run off to do independent checking to prove I was right. (People love to prove they’re right.) I’d gather data and do statistical analysis to prove they were wrong and I was right; and I could prove I was right to my satisfaction pretty often. (It’s amazing how often people can prove they’re right to their own satisfaction—the plaintiff, judge, jury and executioner all in one.) But later I realized I was doing the wrong thing. What I should have been doing was looking in the media for assertions I believed were true and then checking to see if they weren’t really false.

Why?

If I believe the assertion is true, then probably so do many others, if not the overwhelming bulk of investors. Maybe everyone. And if we’re all wrong, there’s real power there. If I can prove I’m wrong and most everyone else is also wrong, then I’ve got some useful information. I can bet against everyone knowingly. I’ve got one provable form of knowing something others don’t.

Suppose I believe factor X causes result Y. If I believe it, probably most other folks do, too. But if I’m wrong, most everyone else is wrong. When X happens, people will move to bet on Y happening. Suppose I can learn X doesn’t cause Y. That means something else is causing Y. That means after X happens, Y happens sometimes, but it’s purely random to X’s existence. Now when X happens, people will still move to bet on Y happening, but I can bet against Y happening, and I’ll be right more often than I’m wrong. (If I can figure out what actually causes Y, I can take a big step further, but we don’t cover that step until Chapter 2 and Question Two.)

With our P/E notion, we can see one such perfect example. Say the market’s P/E goes up—a lot. Normal investors notice and conclude risk has risen and future return is lower and bet against the market doing well. Sometimes stocks won’t do well, but more often than not stocks will be just peachy because the P/E by itself tells you nothing about market risk and direction.

When I see a high-P/E market and fear of it, I can bet against the market falling. Sometimes, like 2000, it won’t work. But more often, like 1996, 1997, 1998, 1999, 2003 and 2009, it will. I don’t expect you to believe the P/E thing right now. Right now, I expect you to believe the traditional mythology about P/Es and not even be very interested in challenging it. (We get to that later in detail.) For now, I just want you to accept in your bones if you can learn an accepted mythology is actually false, you can bet against it and win more often than you lose.

Using Question One

A good way to think about successful investing is it’s two-thirds not making mistakes and one-third doing something right. Hippocrates is frequently credited with the phrase, First, do no harm, and it’s a good investment principle.

To first do no harm, you must think about what you believe and ask yourself whether it’s correct and factually accurate. Go crazy. Question everything you think you know. Most people hate doing this, which gives you a real advantage over them. As stated in this chapter’s title, this is the first question: What do you believe that is actually false?

Asking Question One helps only if you can be honest with yourself. Many people, particularly in investing, are constitutionally incapable of contemplating they’re ever wrong. They will tell you they do well and likely hoodwink themselves into believing it—but they don’t. And they never subject themselves to reliable independent analysis. You must accept that you and the pundits and professionals from whom you glean information can be and probably are wrong about many basic beliefs. Me too!

Have you ever presented such a question to yourself about capital markets? Asking yourself if what you believe is actually wrong requires introspection. As humans, we’re hardwired to be overconfident. This is hardly a new development. Behavioralists will tell you our Stone Age ancestors had to be overconfident to hunt giant beasts each day armed merely with stone-tipped sticks. If they practiced introspection and came to the rational conclusion that tossing a flint-tipped branch at a buffalo was utter lunacy, they, their families and their communities would have starved. In fact, overconfidence—the belief you can do something successfully when rationality would argue otherwise—is basic to human success in most fields and necessary to our successful evolution as a species. However, it hurts tremendously when it comes to capital markets. (More on this in Chapter 3.)

Just so, investors are loath to question generally accepted knowledge. If we started doing so, we might soon realize the market exists solely to humiliate us as much as it can for as long as it can for as many dollars as it can. I refer to the market by its proper name, The Great Humiliator (TGH for short). I’ve come to accept my goal is to interact with TGH without getting humiliated too much.

TGH is an equal-opportunity humiliator. It doesn’t care if you’re rich or poor, black or white, tall or fat, male or female, amateur or an Olympian. It wants to humiliate everyone. It wants to humiliate me and you, too. To be frank, I think it wants to humiliate me more than it does you. You’re fun to humiliate, but if you’re fun, I’m more fun. I’m (probably) a more public figure than you and therefore a bigger TGH target. Think how much TGH would love to humiliate Warren Buffett. The bigger you are, the more TGH wants you. But in reality, TGH wants to get everyone and does a pretty good job at getting them all eventually. Can’t be sated!

How do you, personally, give TGH the most fun? By making the most bets you can based on the same information everyone else has. How do you spoil the fun for TGH? By restricting bets you make to things you think you actually know that others don’t.

Practice using Question One the same way I should have—by scanning the media for things asserted you believe. Make a list of them. They can be about single stocks, whole markets, currencies or anything. Make a list of anything influencing your decisions, whether on single stocks, asset allocation, anything.

Make note of decisions you’ve made not supported by data or any other information. Underneath there somewhere is something you believe—might be right or might be wrong. Be particularly wary of making a decision simply because of something you know others agree with. Highlight, underline and asterisk decisions prompted or based on common investor catechism. Ask what evidence you figured out for yourself supporting these beliefs. Is there any? For most investors, there isn’t much.

Common Myths You Believe In, Too

For example, you may hold a stock with a high P/E ratio. You believe a high P/E signals an overvalued stock, so you decide to dump the stock and buy one with a lower P/E. It’s a fairly rational decision you may have made countless times before, and one many people would agree is rational.

But are high P/Es bad for single stocks or the market? Have you personally checked the data? If you have asked the question, where did you find the answer? Did you look at the numbers, or did you rest easy because conventional wisdom or some big-name guru endorsed your belief?

Take another scenario. You hold a stock that does well in rising markets but badly in falling ones—a typical, highly volatile stock. However, you know the US federal government is running a growing budget deficit—not only a deficit, but a historically high deficit and one that can’t go on forever. You know federal budget deficits left unchecked are bad for the economy and, in turn, bad for the stock market. All that debt caused by the deficit must be paid back by future generations, and the market will reflect that sooner or later, right? The burden of the deficit has long-term rippling implications, holding down growth and earnings. The deficit has grown to such a size you know a bear market looms eventually. In that environment, your highly volatile stock would do badly, and so you sell.

But how do you know budget deficit peaks are followed by poor stock performance? Is it true? Most folks won’t ask the question or check history. If they did, they would be sanguine about stocks rather than fearful. Historically, big budget deficits in America and around the world have been followed by materially above-average stock market returns. Don’t fear deficits—it is big budget surpluses that have been soon followed by bad markets.

That doesn’t make intuitive sense to you. Deficits must be bad and surpluses good, right? After all, the word deficit has the same Latin root as deficient—and that must be bad. Most folks won’t challenge their own beliefs on these kinds of subjects. The notion that big deficits are bad is overwhelming. Few beliefs have as much broad acceptance from professionals, nonprofessionals and folks from both ends of the political spectrum alike. A good way to get the proletariat on your side at a political rally is to vow to lower budget deficits. It’s a crowd pleaser.

Here’s a baker’s dozen of some general beliefs you probably hold, or at least most people do. We’ve already covered two:

1. High-P/E markets are riskier than low P/E markets.

2. Big government budget deficits are bad.

Let’s think about some more:

3. A weak US dollar is bad for stocks.

4. Rising interest rates are bad for stocks. Falling rates are good.

5. A tax cut causes more debt, which is bad for stocks.

6. Higher oil prices are bad for stocks and the economy.

7. Stocks do well when the economy does well.

8. Stock markets do better in countries with faster-growing economies than slower ones.

9. Small stocks do better than big ones.

10. Stocks of firms that grow more do better than those that don’t.

11. Cheaper stocks do better than less cheap stocks.

12. Big trade deficits are bad for stock markets.

13. America has way too much debt.

They’re all familiar to you. This is just a short list—a subset of a much bigger list—of views most folks believe that are partly or wholly false. For example, the notion America is way too heavily in debt is backward. As I say that, you may be shriekingly dismissive, or maybe the statement makes you mad. It challenges your belief set. If the statement makes you either dismissive or mad, you really need the rest of this book. The most standard reaction to someone stating your belief is wrong is to be dismissive and, if further confronted, to get mad.

Anger is a very good warning sign because anger is always, always about fear. Angry people usually don’t know they’re fearful. If you’re dismissive or angry, you must question yourself to see how and why you concluded your belief was right in the first place. Was it mythology? Was it basic bias? Are you right or not? Sometimes the items in this list and others beyond it are part true and part false, depending on surrounding circumstances. (We look at all of these and more later on.) But the most obvious question is: Why would you believe any of these statements?

I’d say you believe myths mostly because of two facts: (1) They make common sense, and you aren’t typically prone to challenge your own common sense. (2) People around you tend to agree these things are true, and you aren’t prone to challenge widely held views.

Let’s Prove You’re Either Right or Wrong (or Really, Really Wrong)

As you attempt to debunk investor mythology using Question One, you will find three basic results. Either you were right all along (which may happen less frequently than you might have hoped), or you were wrong, or you were really, really wrong. Any of these outcomes is ok because it tells you how to bet better, later.

Let’s examine more closely the instances when you’re wrong. You and most of your fellow investors (amateur and professional) often believe something is causal—X happens because of Y—but in reality, there is no correlation at all. By now you’re willing to embrace that can happen, or you would have stopped reading this book. The example we debunk is the aforementioned commonly held belief high-P/E stock markets are risky with subsequent below-average returns. As previously mentioned, it turns out high-P/E markets aren’t predictive of poor returns—not even remotely. In fact, historically, they’ve led to some pretty good returns. What’s more, low-P/E markets aren’t predictive of good returns either.

The Mythological Correlation

Forgetting for now why the P/E myth is so easy to buy into, we know people overwhelmingly do believe high-P/E markets predict below-average returns and above-average risk.

But if it were true, you could show some form of high statistical correlation between the claimed cause and result. A statistician will say you can have high correlation between two things out of quirky luck with no causation. But the same statistician will tell you that you can’t have causation without high correlation (unless you run into scientific nonlinearity, which doesn’t happen in capital markets to my knowledge—but you could check on your own with the Three Questions when you’re finished with this book). When a myth is widely accepted, you will find low correlations coupled with a great societal effort to demonstrate, accept and have faith in correlations that don’t really exist.

Investors will root out evidence supporting their favorite myths and create justifications for their belief—factor X causes result Y—while ignoring a mountain of evidence that X doesn’t cause Y at all. Now let’s suppose everyone is of good intent. Still, even with the best of intentions, it’s easy for people to latch onto evidence confirming their prior biases and ignore evidence contradicting their views. Looking for evidence to support your pet theory is human. Accepting evidence to the contrary is no fun at all. This is done in varying ways. One way is to look at a particular time period verifying the false belief and ignore other periods. Another is to redefine either X or Y in a bizarre way so the statistics seemingly prove the point and then generalize afterward about X and Y without the bizarre definitions. Discoveries of data supporting popular myths become popular discoveries.

Why High P/Es Tell You Nothing at All

A great example of redefining X or Y is the now-famous study by John Y. Campbell of Harvard and Robert J. Shiller of Yale.¹ Their paper didn’t introduce a new idea because fear of high P/Es had been around forever. Their study merely introduced a new delivery of data confirming the view high-P/E periods are followed by below-average returns, an already widely held belief.

This was actually a better redo of a study they presented in 1996. But this 1998 publication got very popular, very fast because it supported what everyone already believed with new statistical documentation. Campbell and Shiller were and are noted academics. Inspired by the prior study, in 1996 Alan Greenspan first uttered the phrase irrational exuberance relative to the stock market, which reverberated around the world almost overnight and entered our lexicon permanently.

My friend and sometimes collaborator Meir Statman, the Glenn Klimek Professor of Finance at the Leavey School of Business at Santa Clara University, coauthored with me a paper not refuting their statistics, but reframing their approach more correctly with the same data—and you will see P/E levels aren’t predictive at all. We basically asked Question One from beginning to end. Much of what follows stems from our paper Cognitive Biases in Market Forecasts.²

In their study, Campbell and Shiller found high P/Es acted as people always thought they did, leading to below-average returns 10 years later. First, they noted the P/E at the outset of each year and subsequent annual market returns going back to 1872, which is about as far back as we have half-tolerably reliable data. Prior to the inception of the S&P 500’s data in 1926, they used Cowles data, which is an imperfect but generally accepted proxy for pre-S&P 500 years. (All old databases are imperfect. Whenever you’re looking at old data, there is apt to be lots wrong with it, but the Cowles data is the best we have.) Then they graphed the data points on a scatter plot and found a slightly negative trend line.

I’ve included the years since their paper (and updated through 2010) to ensure our findings are relevant today. But you’d get the same basic effect if I hadn’t. The negatively sloping trend line shouldn’t influence you. You plainly see the scatter points aren’t particularly well grouped around it. The scatter plot is, well, scattered—sort of like a shotgun blast in a mild wind.

The key issue I had with the study was Campbell and Shiller based their work on an odd definition of P/E—not one you intuitively leap to. They created a price-smoothed earnings ratio.³ The newly defined P/E divided the price per share by the average of real earnings over the prior 10 years.⁴ (Real means adjusted for inflation.) Fair enough, but that isn’t what you think of when you think P/E, right?

But, if so, what definition of inflation would you use? I bet you would use something like the Consumer Price Index (CPI). (The CPI comes up as one of your first results when you Google inflation.) Ironically, they chose an esoteric wholesale price index. Again, not what you might default to. So instead of what you think of as P/E, they used a 10-year rolling average based on inflation adjustments based on an inflation index most wouldn’t think of. Got it?

With a normally defined P/E, as you would think of it, there isn’t much of a statistical fit at all. However, Campbell and Shiller’s engineered P/E gave a result consistent with what society always believed—that high P/E means low returns, high risk. And the world seemingly loved it.

In statistics, a calculation called an R-squared shows the relative relatedness of two variables—how much of one variable’s movement is caused by the other. (It sounds complicated, but it’s not—I show you how to find a correlation coefficient and an R-squared in Appendix A.) For their study, Campbell and Shiller got an R-squared of 0.40.⁵ An R-squared of 0.40 implies 40% of subsequent stock returns are related to the factor being compared—in this case, their reengineered P/E. Statistically, not a bad finding (although not an overwhelming one). Though not a whopping endorsement of their theory, this finding still supports their hypothesis.

Note: Campbell and Shiller’s study, tepid support or not, became wildly popular because it supported the view society had long held. If you present data violating society’s myths, those data won’t be met with great popularity. That’s nice because when you discover the truth, the world won’t be trying to take it away from you in a hurry.

By using the same basic data and traditional notions of P/Es at the start of each year from 1872 to 2010 and actual 10-year subsequent returns, updating this study for this edition, we get an R-squared of 0.25. The P/E only potentially explains 25% of 10-year returns—statistically pretty random. Something else entirely, or some group of other variables, explains the other 75% of price returns. I wouldn’t make a bet on an R-squared of 0.25, and neither should you. Said another way, Campbell and Shiller’s R-squared was 0.40 and ours was 0.25—so a big chunk of their result was based on how they defined P/E differently.

This myth wasn’t hard to debunk. You can arrive at the same general conclusion with Google Finance and an Excel spreadsheet. When it isn’t a myth and it’s real, you will find you need no fancy statistical reengineering and no fancy math in your analysis.

But there’s yet another issue. Even if it were valid, who cares about views of subsequent 10-year returns? Investors mainly want to know how to get positioned for this year and next, the now and the soon, not for 10 years from now. Would you really have cared what the next 10-year return was in 1996, when the next four years rose massively only to be followed by a big bear market? Would you have wanted to miss the big up years in a row, and would you have been content to hold on through the big down years? When you look at simple P/Es on a shorter-term basis, the high-P/E-is-risky thesis falls apart completely, as we shall see.

What’s more, forecasting long-term stock returns is a near impossibility because stock prices in the long term are the result primarily of shifts in far-distant levels of the supply of equities, which in today’s state of knowledge (or ignorance), no one knows how to address. Some of my academic friends get angry when I bring this up. But remember, when anyone gets angry, they are afraid and just can’t quite put their finger on their fear. In this case, I think it’s because very little real scientific work has been done analyzing shifts in supply and demand for securities. Yet, by definition, shifts in supply and demand are what determine pricing. There are great future advances to be made here, but so far, the progress is minimal despite supply and demand being basic to economics. (We get to supply and demand for securities in Chapter 7.)

For now, let’s take a look at our scatter plot again, this time using normal, non-engineered P/Es and subsequent one-year returns from 1872 through 2010 (see Figure 1.2). Note we have a much shallower negative trend line, and the scatter points are even less cooperative. This is our same shotgun blast with a few stray pellets. Does this indicate any sort of correlation at all? With an R-squared of 0.01, the answer is no. If an R-squared of 0.25 is pretty random, an R-squared of 0.01 is randomness itself—pure, perfect randomness.

Finding a correlation where simply none exists is pretty creative; and simply, none exists here. To begin debunking myths on your own, you don’t need a super computer and a Stephen Hawking doppelganger (that’s probably illegal anyway). If you need ultra-complicated math to support the existence of a market myth, your hypothesis is probably wrong. The more jury-rigging and qualification your analysis needs, the more likely you’re forcing your results to support your hypothesis. Forced results are bad science.

If Not Bad, Can They Be Good?

We have shown there’s no correlation between high P/Es and poor stock results (or good ones). Even in light of such damning evidence, some may be reluctant to let go of the high-P/E-equals-bad-stocks doctrine. Consider this another way: It may further shock and appall you to learn years with higher P/Es had some excellent returns. Moreover, the one-year returns following the 13 highest P/E ratios weren’t too shabby—some negative years, but also some big positive years. This isn’t statistical but should give you pause.

Here’s how I arrived at the bell curve. We noted the broad market’s P/E each January 1 going back to 1872 and ranked each year from low P/E to high P/E. Then we grouped them into intervals creating the familiar bell curve-like shape—with otherwise unrelated years falling into buckets according to their P/Es. The normal P/Es fall in the fat part of the bell curve, while the high and low P/Es fall on the edges.

When you note the P/E ratios for the past 139 years along with the subsequent market return, some empirical truths emerge. Most startling? Most double-digit calendar-year stock market declines—the monster drops everyone fears—occurred when P/Es were below 20, not when they were very high.

In the past 139 years, there were 20 times the US market’s total return was negative more than 10%. Sixteen times—80% of those most negative years—were on the middle-to-low end of the P/E range (based on the bell curve). Fifteen (75%) happened in the fat part of the curve—on normal P/Es. Hardly fodder for a myth. Anyone can get these data off the Internet. Anyone can array them. It doesn’t take fancy math. It just takes a little effort. But most people don’t ask, so they don’t try. And since they don’t try, the myth still exists.

So big double-digit drops don’t automatically follow high-P/E markets. But since the myth is so widely and rigidly believed, could there be some kernel of truth to it? For example, high-P/E markets must fall more often than those with low P/Es, even if they aren’t the monster drops. Right? Well, no! P/Es were below 22.8 in 116 of those years, and the market finished in negative territory 32 times (27.5%).

Of those 17 years when P/Es were 22.8 or higher—the historically high end of the P/E range—the market ended down seven times (30.4%). Neither high- nor low-P/E markets did materially worse.

You’ve seen the data. You’re henceforth unshackled from this investing old wives’ tale.

Here’s a simple test you can use repeatedly. Someone tells you X causes Y in America’s markets—like the P/E example—and even has data to demonstrate it’s true. If it’s really true in America, then it must also be true in most foreign developed markets. If it isn’t similarly true in most other developed Western markets, it isn’t really true about capitalism and capital markets and, hence, isn’t really true about America—just a chance outcome. I’ll not belabor you with the data here—this book already has too many visuals—but if you take the same bell curve approach we used for America’s stock market and apply it to foreign markets, the only country where low-P/E markets seemed materially better is Britain—and that is based on a few, relatively big years. Elsewhere, you get the same randomness as in America.⁶ Whenever anyone tells you something works a certain way in America, a good cross-check is to see if it also works outside America. Because if it doesn’t, it doesn’t really work robustly in America either!

Some will say, You must see the high-P/E problem in the right way. (Warning: a precursor to a reengineering attempt to support a myth, and it likely won’t hold.) For example, they may agree it isn’t just that a high P/E is worse than a low P/E, but when you get over a certain P/E level, the risk skyrockets, and when you get under a certain P/E, it plummets.

For example, they may assert market P/Es over 25 are bad and P/Es under 15 good and everything in between is what confuses everyone—throwing the averages off, leading you to not see things the way they would have you see them. Fair enough! That’s easy to test. You take all the times when the market had a P/E over 25 and envision we sold and then bought back at some level— you pick it, I don’t care what it is as long as you apply it consistently. It turns out, historically, regardless of the level picked, none really beats a long-term buy-and-hold in America.

The same is true overseas (except, again, in Britain, where you can make a weak case a low P/E has had a variety of approaches seeming to work—but only in Britain, which is probably just coincidence—and if you throw out a very few, very big years in Britain from a very long time ago, it falls apart there, too).

Suppose you sell when the market’s P/E hits 22 and buy when it falls to 15. That approach lags a simple buy and hold. Suppose you change the 22 to 23. Still lags! How about dropping the 15 to 13 or raising it to 17? Still lags. What’s more, there isn’t a buy-and-sell approach that works overseas.

You may disbelieve all this. Great. Prove I’m wrong. To prove it, you must find a buy-and-sell rule based on simple P/E beating the market with one-, two- and three-year returns. It must work basically the same way in a handful of foreign developed markets and if you start or end your game on different dates. Try to find it. Maybe you’re better than I am, but I looked and looked and can’t find it in any way anyone would believe.

Every time a high-P/E market leads to very bad returns, like in 2000, 2001 and 2002, you will find a comparable number of examples where it does well, like 1997, 1998, 1999, 2003 and 2009. There is simply no basis for this myth.

Always Look at It Differently

Investors fall prey to myth because they’re used to seeing investing truisms in accepted and normal ways—as they were taught. Once you start thinking even a bit differently—not in a complicated way, just differently, like graphing a bell curve or looking for the same phenomenon overseas—myths tend to fall apart. Whenever you’re confirming an investing belief, try it from a fresh angle. Go crazy. Be creative. Flip things on their heads, backward and inside out. Hack them up and go over their guts. Instead of trying to be intuitive, think counterintuitively—which may turn out to be much more intuitive.

For fun, let’s look at why, intuitively, high P/Es don’t spell disaster for stocks. Most investors look at stocks with high P/Es and assume their prices are too high relative to the companies’ earnings. If a price is proportionately much greater than earnings (so goes the thinking), the stock must be overpriced; what goes up must come down. What investors forget is the P isn’t the only moving variable in the P/E.

In years following high-P/E markets, earnings often rose faster than share prices. And often after low-P/E years, we ran into unexpected rough economies where earnings vanished. In fact, in 1929, the most famous market peak of all time, P/Es were low, not high, because the soon-to-disappear earnings were too high in 1929, making the P/E low.

When we buy stocks, we’re buying future earnings. At some times we’re willing to pay more than at others. In high-P/E markets, earnings often exceed expectations (as in 2003 and 2009), and the market prices in higher earnings before we can see them coming. Just by considering what is happening with the denominator side of the P/E—looking at it differently—you can reason for yourself why the myth is wrong.

The myth that high-P/E markets are dangerous and low-P/E markets are safe persists. But anyone with a dial-up modem and a pencil can see high-P/E years are, in themselves, not any worse than lower-P/E periods. Why does this myth persist? Because fundamentally, TGH is perverse and counterintuitive. It can be painful to accept whatever is fueling your water-cooler debates is wrong or already priced into markets. It’s humbling but true.

How Would Your Grandparents Think About It?

Now I’ll steal a page from Chapter 3 and focus on how our brains blindside us on the P/E issue. There’s a genetic reason people fear high P/E markets. I can’t prove this, but I believe it’s true. And you can’t disprove it. It’s a pretty different way to see this dilemma. You inherited your genes and the information processor that is your brain from your parents, as did they from theirs. Your far-distant ancestors had brains adept at processing certain types of information—that which related to problems they encountered related to passing on their genes successfully. Were that not true, you and I wouldn’t be here. The folks back then who didn’t process information well relative to those problems don’t have descendants walking the earth now.

Reviews

By far the smartest investment book ever written. Fisher has been listed on the Forbes' Richest Persons list and the son of the late, great Philip Fisher... No slouch in the market himself. Fisher shows why he is one of the greatest money managers of all time. Insightful about what he teaches and how he thinks about the world markets and the relationships between them all. Five Stars!